Case-Based Relational Learning of Expressive Phrasing in Classical Music
Identifieur interne : 001C48 ( Main/Exploration ); précédent : 001C47; suivant : 001C49Case-Based Relational Learning of Expressive Phrasing in Classical Music
Auteurs : Asmir Tobudic [Autriche] ; Gerhard Widmer [Autriche]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2004.
English descriptors
Abstract
Abstract: An application of relational case-based learning to the task of expressive music performance is presented. We briefly recapitulate the relational case-based learner DISTALL and empirically show that DISTALL outperforms a straightforward propositional k-NN on the music task. A set distance measure based on maximal matching – incorporated in DISTALL – is discussed in more detail and especially the problem associated with its ‘penalty part’: the distance between a large and a small set is mainly determined by their difference in cardinality. We introduce a method for systematically varying the influence of the penalty on the overall distance measure and experimentally test different variants of it. Interestingly, it turns out that the variants with high influence of penalty clearly perform better than the others on our music task.
Url:
DOI: 10.1007/978-3-540-28631-8_31
Affiliations:
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